{
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   "source": [
    "# Workflow Scheduling Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d948036d",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "We consider the following optimization problem (based on the formulation in [[1](#TaskWorkflow)]) : we have $W(t)$ available workers or resources and $N$ jobs that each job $j$ requires $T(j)$ time, and takes $r(j)$ resources to be completed. Given a set of dependencies for each job, where we know which job depends on the completion of others before starting, we need to determine the order of job completion that minimizes the total execution time.\n",
    "### Assumptions\n",
    "- a job may occupy only a single time slot at most: $\\forall j \\,\\,\\, T(j) = 1$\n",
    "- given sufficient resources, jobs can be started in the same time slot\n",
    "- all resources are identical and the amount of available resources at a current time step is given by the number $W(t)$\n",
    "- jobs can only be started if all parent jobs are completed.\n",
    "\n",
    "### Possible extensions\n",
    "- Separate resources into multiple categories with jobs requiring different types\n",
    "- Jobs that take more than one operation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966c9605-c442-436f-b01a-a76004bbd9b2",
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   "source": [
    "## Mathematical Modeling\n",
    "\n",
    "The input of the model is\n",
    "\n",
    "\n",
    "We define a binary variable for the optimization problem: an $t_{max}\\times N$ matrix $x$ such that\n",
    "$\\begin{aligned}\n",
    "x_{tj} =\n",
    "\\begin{cases}\n",
    "      1 & \\text{job } j \\text{ is done on the } t_\\text{th} \\text{ time slot} \\\\\n",
    "      0 & \\text{else}\n",
    "\\end{cases}\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "We have 3 constraints:\n",
    "\n",
    "(1) All jobs must be completed exactly once: $\\forall j\\in[1,N] \\,\\,\\, \\sum_t x_{tj}=1$\n",
    "\n",
    "(2) All jobs may use no more than the available resources: $\\forall t\\in[0,t_{max}] \\,\\,\\, \\sum_j x_{tj} r_j \\leq W_t$\n",
    "\n",
    "(3) All dependent jobs must have parent jobs completed before starting $x_{t_1j_1} x_{t_2j_2} = 0 \\,\\,\\, \\forall j_1, t_1\\leq t_2, j_2 \\text{ depends on } j_1$\n",
    "\n",
    "\n",
    "The objective function to minimize is the total cost function:\n",
    "$$\n",
    "\\min_{x} \\sum_{t} x_{tN}\\cdot t\n",
    "$$\n",
    "which favors schedules that are done early."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dfdc09-4920-4966-a738-d8a8e7b4dc4e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Defining the optimization model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd946990-d0dc-4f29-aa15-949819b8d09f",
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    "execution": {
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   "source": [
    "from typing import List, Tuple, cast  # noqa\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np  # noqa\n",
    "import pandas as pd\n",
    "import pyomo.environ as pyo\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c731afb0-f94b-43b1-9cdb-1de8bb3a1f46",
   "metadata": {
    "execution": {
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   "source": [
    "def define_workflow_problem(\n",
    "    G, num_timeslots, available_capacities, work_loads\n",
    ") -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel(\"task_scheduling\")\n",
    "\n",
    "    timeslots = range(num_timeslots)\n",
    "    assert len(timeslots) == len(available_capacities)\n",
    "    works = range(len(G.nodes))\n",
    "    assert len(works) == len(work_loads)\n",
    "\n",
    "    last_works = [node for node in G.nodes if G.out_degree(node) == 0]\n",
    "    assert len(last_works) == 1\n",
    "    last_work = last_works[0]\n",
    "    # works with no dependancies\n",
    "    root_works = [node for node in G.nodes if G.in_degree(node) == 0]\n",
    "\n",
    "    model.x = pyo.Var(timeslots, works, domain=pyo.Binary)\n",
    "\n",
    "    @model.Constraint(works)\n",
    "    def all_works_are_done(model, i):  # constraint (1)\n",
    "        return sum(model.x[t, i] for t in timeslots) == 1\n",
    "\n",
    "    @model.Constraint(timeslots)\n",
    "    def capacity_is_valid(model, t):  # constraint(2)\n",
    "        return (\n",
    "            sum(model.x[t, i] * work_loads[i] for i in works) <= available_capacities[t]\n",
    "        )\n",
    "\n",
    "    @model.Constraint(works, works, timeslots, timeslots)\n",
    "    def works_done_by_their_order(model, i, j, t1, t2):  # constraint (3)\n",
    "        if G.has_edge(i, j) and t1 >= t2:\n",
    "            return model.x[t1, i] * model.x[t2, j] == 0\n",
    "        return pyo.Constraint.Feasible\n",
    "\n",
    "    # eliminate all timeslots that are not possible for the task in order to save qubits\n",
    "    distances_from_ends = nx.floyd_warshall_numpy(G)\n",
    "    distances_from_roots = nx.floyd_warshall_numpy(G.reverse())\n",
    "\n",
    "    @model.Constraint(works, timeslots)\n",
    "    def eliminating_rule(model, i, t):\n",
    "        end_distance = int(np.min(distances_from_ends[i, last_works]))\n",
    "        start_distance = int(np.min(distances_from_roots[i, root_works]))\n",
    "        if (t < start_distance) or (t >= len(timeslots) - end_distance):\n",
    "            return model.x[t, i] == 0\n",
    "        return pyo.Constraint.Feasible\n",
    "\n",
    "    # minimize the end time of the last work\n",
    "    model.cost = pyo.Objective(\n",
    "        expr=sum(model.x[t, last_work] * (t + 1) for t in timeslots), sense=pyo.minimize\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
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   "cell_type": "markdown",
   "id": "9c415675-cc07-48bd-8b93-333d8f2a6787",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Visualization helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abb38448",
   "metadata": {
    "execution": {
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   "source": [
    "import random\n",
    "\n",
    "\n",
    "def plot_graph(solution=None, ax=None):\n",
    "    if solution is not None:\n",
    "        # determine how many tasks start in each timeslot\n",
    "        num_tasks = [sum(solution[t]) for t in range(num_timeslots)]\n",
    "        max_tasks = max(num_tasks)\n",
    "\n",
    "        pos = {}\n",
    "\n",
    "        # find all the tasks that start in particular start time\n",
    "        for start in np.nonzero(solution.sum(axis=1))[0]:\n",
    "            locations = solution[start].nonzero()[0]\n",
    "            pos.update(\n",
    "                {\n",
    "                    n: (start, i + (max_tasks - num_tasks[start]) / 2)\n",
    "                    for i, n in enumerate(locations)\n",
    "                }\n",
    "            )\n",
    "    else:\n",
    "        pos = {\n",
    "            node: (order, random.random())\n",
    "            for order, node in enumerate((nx.topological_sort(G)))\n",
    "        }\n",
    "\n",
    "    options = {\n",
    "        \"font_size\": 12,\n",
    "        \"node_size\": 1000,\n",
    "        \"node_color\": \"white\",\n",
    "        \"edgecolors\": \"black\",\n",
    "        \"linewidths\": 3,\n",
    "        \"width\": 3,\n",
    "    }\n",
    "    # G = nx.DiGraph(edges)\n",
    "    nx.draw_networkx(G, pos, ax=ax, **options)\n",
    "\n",
    "    # Set margins for the axes so that nodes aren't clipped\n",
    "    if ax is None:\n",
    "        ax = plt.subplot()\n",
    "    ax.margins(0.20)\n",
    "    if solution is not None:\n",
    "        ax.set_title(\"Suggested sequence\")\n",
    "    ax.set_xlabel(\"Timeslot\")\n",
    "\n",
    "\n",
    "def plot_assignments(solution, ax=None):\n",
    "    if ax is None:\n",
    "        ax = plt.subplot()\n",
    "    ax.pcolormesh(solution.T, edgecolors=\"w\", linewidth=4, cmap=\"OrRd\")\n",
    "    ax.set_aspect(0.8)\n",
    "    ax.set_xlabel(\"Timeslot\")\n",
    "    ax.set_ylabel(\"Task Number\")\n",
    "    ax.set_title(\"Task assignment\")\n",
    "\n",
    "\n",
    "def plot_resource_graph(solution=None, ax=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "\n",
    "    x_pos = np.arange(len(capacities))\n",
    "    if solution is not None:\n",
    "        ax.set_title(\"Utilization\")\n",
    "        num_resources = [np.dot(solution[t], workloads) for t in range(num_timeslots)]\n",
    "        ax.bar(x_pos + 0.1, num_resources, label=\"used resources\", color=\"r\", width=0.5)\n",
    "    ax.bar(x_pos, capacities, label=\"available_resources\", color=\"g\", width=0.5)\n",
    "    ax.set_xticks(x_pos)\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Timeslot\")\n",
    "    ax.set_ylabel(\"Resources\")\n",
    "\n",
    "\n",
    "def plot_workloads(ax=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    ax.set_title(\"Work Loads\")\n",
    "    x_pos = np.arange(len(workloads))\n",
    "    ax.bar(x_pos, workloads, width=0.5)\n",
    "    ax.set_xticks(x_pos)\n",
    "    ax.set_xlabel(\"Jobs\")\n",
    "    ax.set_ylabel(\"Required Resources\")\n",
    "\n",
    "\n",
    "def is_printable_solution(solution):\n",
    "    return np.array_equal(solution.sum(axis=0), np.ones(solution.shape[1]))\n",
    "\n",
    "\n",
    "def plot_workflow(solution=None):\n",
    "    if solution is None:\n",
    "        fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "        plot_resource_graph(ax=axes[0])\n",
    "        plot_graph(ax=axes[1])\n",
    "        plot_workloads(ax=axes[2])\n",
    "\n",
    "    else:\n",
    "        if is_printable_solution(solution):\n",
    "            fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "            plot_resource_graph(solution, axes[0, 0])\n",
    "            plot_workloads(axes[0, 1])\n",
    "            plot_assignments(solution, axes[1, 1])\n",
    "            plot_graph(solution, axes[1, 0])\n",
    "        else:\n",
    "            # illegal solution\n",
    "            fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "            plot_resource_graph(solution, axes[0])\n",
    "            plot_workloads(axes[1])\n",
    "            plot_assignments(solution, axes[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd4aeaa-1724-4c27-a905-73ec10dee863",
   "metadata": {},
   "source": [
    "## Initialize specific problem instance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b25ea7c-6c33-4215-8ae3-370d2bca0fb3",
   "metadata": {},
   "source": [
    "We create a workflow dependecies graph. For the small instance, all timeslots capacities and work loads are equal to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "829468eb",
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   "source": [
    "def small_example():\n",
    "    G = nx.DiGraph()\n",
    "\n",
    "    nodes = range(4)\n",
    "    edges = [(0, 1), (1, 3), (2, 3)]\n",
    "    G.add_nodes_from(nodes)\n",
    "    G.add_edges_from(edges)\n",
    "    num_timeslots = len(G.nodes) - 1\n",
    "    capacities = 3 * np.ones(num_timeslots)\n",
    "    workloads = np.ones(len(nodes))\n",
    "\n",
    "    return (\n",
    "        define_workflow_problem(\n",
    "            G, num_timeslots, available_capacities=capacities, work_loads=workloads\n",
    "        ),\n",
    "        G,\n",
    "        num_timeslots,\n",
    "        capacities,\n",
    "        workloads,\n",
    "    )\n",
    "\n",
    "\n",
    "def large_example():\n",
    "    G = nx.DiGraph()\n",
    "\n",
    "    nodes = range(6)\n",
    "    edges = [(0, 1), (0, 3), (0, 2), (2, 4), (3, 4), (1, 5), (4, 5)]\n",
    "    workloads = [1, 3, 2, 2, 1, 1]\n",
    "    capacities = [1, 3, 4, 3, 1]\n",
    "    G.add_nodes_from(nodes)\n",
    "    G.add_edges_from(edges)\n",
    "\n",
    "    num_timeslots = len(capacities)\n",
    "\n",
    "    return (\n",
    "        define_workflow_problem(\n",
    "            G, num_timeslots, available_capacities=capacities, work_loads=workloads\n",
    "        ),\n",
    "        G,\n",
    "        num_timeslots,\n",
    "        capacities,\n",
    "        workloads,\n",
    "    )"
   ]
  },
  {
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    {
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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tasks_model, G, num_timeslots, capacities, workloads = small_example()\n",
    "plot_workflow()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74179056-f8a7-4a9a-8afe-8055c1e93107",
   "metadata": {},
   "source": [
    "### The resulting Pyomo model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca3c505d-0392-4311-a586-295a92390972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:27.483785Z",
     "iopub.status.busy": "2024-05-07T14:54:27.483315Z",
     "iopub.status.idle": "2024-05-07T14:54:27.510333Z",
     "shell.execute_reply": "2024-05-07T14:54:27.509607Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 Set Declarations\n",
      "    all_works_are_done_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    4 : {0, 1, 2, 3}\n",
      "    capacity_is_valid_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    eliminating_rule_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain                                            : Size : Members\n",
      "        None :     2 : eliminating_rule_index_0*eliminating_rule_index_1 :   12 : {(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2), (3, 0), (3, 1), (3, 2)}\n",
      "    eliminating_rule_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    4 : {0, 1, 2, 3}\n",
      "    eliminating_rule_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    works_done_by_their_order_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain                                                                                                                                  : Size : Members\n",
      "        None :     4 : works_done_by_their_order_index_0*works_done_by_their_order_index_1*works_done_by_their_order_index_2*works_done_by_their_order_index_3 :  144 : {(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 1, 0), (0, 0, 1, 1), (0, 0, 1, 2), (0, 0, 2, 0), (0, 0, 2, 1), (0, 0, 2, 2), (0, 1, 0, 0), (0, 1, 0, 1), (0, 1, 0, 2), (0, 1, 1, 0), (0, 1, 1, 1), (0, 1, 1, 2), (0, 1, 2, 0), (0, 1, 2, 1), (0, 1, 2, 2), (0, 2, 0, 0), (0, 2, 0, 1), (0, 2, 0, 2), (0, 2, 1, 0), (0, 2, 1, 1), (0, 2, 1, 2), (0, 2, 2, 0), (0, 2, 2, 1), (0, 2, 2, 2), (0, 3, 0, 0), (0, 3, 0, 1), (0, 3, 0, 2), (0, 3, 1, 0), (0, 3, 1, 1), (0, 3, 1, 2), (0, 3, 2, 0), (0, 3, 2, 1), (0, 3, 2, 2), (1, 0, 0, 0), (1, 0, 0, 1), (1, 0, 0, 2), (1, 0, 1, 0), (1, 0, 1, 1), (1, 0, 1, 2), (1, 0, 2, 0), (1, 0, 2, 1), (1, 0, 2, 2), (1, 1, 0, 0), (1, 1, 0, 1), (1, 1, 0, 2), (1, 1, 1, 0), (1, 1, 1, 1), (1, 1, 1, 2), (1, 1, 2, 0), (1, 1, 2, 1), (1, 1, 2, 2), (1, 2, 0, 0), (1, 2, 0, 1), (1, 2, 0, 2), (1, 2, 1, 0), (1, 2, 1, 1), (1, 2, 1, 2), (1, 2, 2, 0), (1, 2, 2, 1), (1, 2, 2, 2), (1, 3, 0, 0), (1, 3, 0, 1), (1, 3, 0, 2), (1, 3, 1, 0), (1, 3, 1, 1), (1, 3, 1, 2), (1, 3, 2, 0), (1, 3, 2, 1), (1, 3, 2, 2), (2, 0, 0, 0), (2, 0, 0, 1), (2, 0, 0, 2), (2, 0, 1, 0), (2, 0, 1, 1), (2, 0, 1, 2), (2, 0, 2, 0), (2, 0, 2, 1), (2, 0, 2, 2), (2, 1, 0, 0), (2, 1, 0, 1), (2, 1, 0, 2), (2, 1, 1, 0), (2, 1, 1, 1), (2, 1, 1, 2), (2, 1, 2, 0), (2, 1, 2, 1), (2, 1, 2, 2), (2, 2, 0, 0), (2, 2, 0, 1), (2, 2, 0, 2), (2, 2, 1, 0), (2, 2, 1, 1), (2, 2, 1, 2), (2, 2, 2, 0), (2, 2, 2, 1), (2, 2, 2, 2), (2, 3, 0, 0), (2, 3, 0, 1), (2, 3, 0, 2), (2, 3, 1, 0), (2, 3, 1, 1), (2, 3, 1, 2), (2, 3, 2, 0), (2, 3, 2, 1), (2, 3, 2, 2), (3, 0, 0, 0), (3, 0, 0, 1), (3, 0, 0, 2), (3, 0, 1, 0), (3, 0, 1, 1), (3, 0, 1, 2), (3, 0, 2, 0), (3, 0, 2, 1), (3, 0, 2, 2), (3, 1, 0, 0), (3, 1, 0, 1), (3, 1, 0, 2), (3, 1, 1, 0), (3, 1, 1, 1), (3, 1, 1, 2), (3, 1, 2, 0), (3, 1, 2, 1), (3, 1, 2, 2), (3, 2, 0, 0), (3, 2, 0, 1), (3, 2, 0, 2), (3, 2, 1, 0), (3, 2, 1, 1), (3, 2, 1, 2), (3, 2, 2, 0), (3, 2, 2, 1), (3, 2, 2, 2), (3, 3, 0, 0), (3, 3, 0, 1), (3, 3, 0, 2), (3, 3, 1, 0), (3, 3, 1, 1), (3, 3, 1, 2), (3, 3, 2, 0), (3, 3, 2, 1), (3, 3, 2, 2)}\n",
      "    works_done_by_their_order_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    4 : {0, 1, 2, 3}\n",
      "    works_done_by_their_order_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    4 : {0, 1, 2, 3}\n",
      "    works_done_by_their_order_index_2 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    works_done_by_their_order_index_3 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    x_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain              : Size : Members\n",
      "        None :     2 : x_index_0*x_index_1 :   12 : {(0, 0), (0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2), (1, 3), (2, 0), (2, 1), (2, 2), (2, 3)}\n",
      "    x_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {0, 1, 2}\n",
      "    x_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    4 : {0, 1, 2, 3}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=12, Index=x_index\n",
      "        Key    : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        (0, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : minimize : x[0,3] + 2*x[1,3] + 3*x[2,3]\n",
      "\n",
      "4 Constraint Declarations\n",
      "    all_works_are_done : Size=4, Index=all_works_are_done_index, Active=True\n",
      "        Key : Lower : Body                     : Upper : Active\n",
      "          0 :   1.0 : x[0,0] + x[1,0] + x[2,0] :   1.0 :   True\n",
      "          1 :   1.0 : x[0,1] + x[1,1] + x[2,1] :   1.0 :   True\n",
      "          2 :   1.0 : x[0,2] + x[1,2] + x[2,2] :   1.0 :   True\n",
      "          3 :   1.0 : x[0,3] + x[1,3] + x[2,3] :   1.0 :   True\n",
      "    capacity_is_valid : Size=3, Index=capacity_is_valid_index, Active=True\n",
      "        Key : Lower : Body                              : Upper : Active\n",
      "          0 :  -Inf : x[0,0] + x[0,1] + x[0,2] + x[0,3] :   3.0 :   True\n",
      "          1 :  -Inf : x[1,0] + x[1,1] + x[1,2] + x[1,3] :   3.0 :   True\n",
      "          2 :  -Inf : x[2,0] + x[2,1] + x[2,2] + x[2,3] :   3.0 :   True\n",
      "    eliminating_rule : Size=6, Index=eliminating_rule_index, Active=True\n",
      "        Key    : Lower : Body   : Upper : Active\n",
      "        (0, 1) :   0.0 : x[1,0] :   0.0 :   True\n",
      "        (0, 2) :   0.0 : x[2,0] :   0.0 :   True\n",
      "        (1, 0) :   0.0 : x[0,1] :   0.0 :   True\n",
      "        (1, 2) :   0.0 : x[2,1] :   0.0 :   True\n",
      "        (2, 2) :   0.0 : x[2,2] :   0.0 :   True\n",
      "        (3, 0) :   0.0 : x[0,3] :   0.0 :   True\n",
      "    works_done_by_their_order : Size=18, Index=works_done_by_their_order_index, Active=True\n",
      "        Key          : Lower : Body          : Upper : Active\n",
      "        (0, 1, 0, 0) :   0.0 : x[0,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 1, 0) :   0.0 : x[1,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 1, 1) :   0.0 : x[1,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 2, 0) :   0.0 : x[2,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 2, 1) :   0.0 : x[2,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 2, 2) :   0.0 : x[2,0]*x[2,1] :   0.0 :   True\n",
      "        (1, 3, 0, 0) :   0.0 : x[0,1]*x[0,3] :   0.0 :   True\n",
      "        (1, 3, 1, 0) :   0.0 : x[1,1]*x[0,3] :   0.0 :   True\n",
      "        (1, 3, 1, 1) :   0.0 : x[1,1]*x[1,3] :   0.0 :   True\n",
      "        (1, 3, 2, 0) :   0.0 : x[2,1]*x[0,3] :   0.0 :   True\n",
      "        (1, 3, 2, 1) :   0.0 : x[2,1]*x[1,3] :   0.0 :   True\n",
      "        (1, 3, 2, 2) :   0.0 : x[2,1]*x[2,3] :   0.0 :   True\n",
      "        (2, 3, 0, 0) :   0.0 : x[0,2]*x[0,3] :   0.0 :   True\n",
      "        (2, 3, 1, 0) :   0.0 : x[1,2]*x[0,3] :   0.0 :   True\n",
      "        (2, 3, 1, 1) :   0.0 : x[1,2]*x[1,3] :   0.0 :   True\n",
      "        (2, 3, 2, 0) :   0.0 : x[2,2]*x[0,3] :   0.0 :   True\n",
      "        (2, 3, 2, 1) :   0.0 : x[2,2]*x[1,3] :   0.0 :   True\n",
      "        (2, 3, 2, 2) :   0.0 : x[2,2]*x[2,3] :   0.0 :   True\n",
      "\n",
      "19 Declarations: x_index_0 x_index_1 x_index x all_works_are_done_index all_works_are_done capacity_is_valid_index capacity_is_valid works_done_by_their_order_index_0 works_done_by_their_order_index_1 works_done_by_their_order_index_2 works_done_by_their_order_index_3 works_done_by_their_order_index works_done_by_their_order eliminating_rule_index_0 eliminating_rule_index_1 eliminating_rule_index eliminating_rule cost\n"
     ]
    }
   ],
   "source": [
    "tasks_model.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8e1b4a-f44e-4cc3-a33f-b28e2af0b0a1",
   "metadata": {},
   "source": [
    "# Solve the optimization model with hybrid classical/quantum QAOA (Quantum Approximate Optimization Algorithm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00699d83-11d2-4cc5-893c-064a3dc5e671",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae6cb113-f759-45dc-9ec9-a6cb7a1266fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:27.513930Z",
     "iopub.status.busy": "2024-05-07T14:54:27.513453Z",
     "iopub.status.idle": "2024-05-07T14:54:29.454386Z",
     "shell.execute_reply": "2024-05-07T14:54:29.453757Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=8, penalty_energy=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15df86cd-50fc-43b4-a8b6-4b89e0fc8dc5",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "242ecd0f-ed55-4aa6-b1da-c81a269c7670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:29.457486Z",
     "iopub.status.busy": "2024-05-07T14:54:29.456731Z",
     "iopub.status.idle": "2024-05-07T14:54:29.460181Z",
     "shell.execute_reply": "2024-05-07T14:54:29.459593Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=20.0, alpha_cvar=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1b3dab-f1ec-4b91-a152-0b23c38dcff3",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a80f90af-35d8-409b-a83c-00235d7aed62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:29.462599Z",
     "iopub.status.busy": "2024-05-07T14:54:29.462112Z",
     "iopub.status.idle": "2024-05-07T14:54:30.104880Z",
     "shell.execute_reply": "2024-05-07T14:54:30.104235Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=tasks_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b15ffa-e385-462d-a4d5-35b1b313d050",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6381873-f619-4b2f-a19f-bdc63c31a633",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:30.107776Z",
     "iopub.status.busy": "2024-05-07T14:54:30.107376Z",
     "iopub.status.idle": "2024-05-07T14:54:30.117781Z",
     "shell.execute_reply": "2024-05-07T14:54:30.117164Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b91d6e9-54e4-4461-8fe8-cb233342a074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:30.120359Z",
     "iopub.status.busy": "2024-05-07T14:54:30.119835Z",
     "iopub.status.idle": "2024-05-07T14:54:30.134202Z",
     "shell.execute_reply": "2024-05-07T14:54:30.133630Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"task_scheduling_problem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5403bf21-87c4-47af-bbaa-bb48c8ffd309",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64ca1eba-a882-489c-bfd0-c3949ab7b3a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:30.137168Z",
     "iopub.status.busy": "2024-05-07T14:54:30.136925Z",
     "iopub.status.idle": "2024-05-07T14:54:34.716618Z",
     "shell.execute_reply": "2024-05-07T14:54:34.715885Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/f5e85b37-3f5f-45a4-851e-c3690030a4cd?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c692172-a935-454e-a874-629ff28183f4",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "971b1d10-53db-4347-92a4-f662476648af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:34.720739Z",
     "iopub.status.busy": "2024-05-07T14:54:34.720050Z",
     "iopub.status.idle": "2024-05-07T14:54:38.216221Z",
     "shell.execute_reply": "2024-05-07T14:54:38.215453Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c496ee-65ba-4852-a35c-0e9515afe1ac",
   "metadata": {},
   "source": [
    "## Analyzing the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31245571-1951-4c3f-a917-c5193ed815c4",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "491b81a0-4813-4e4b-bed9-6248441b20dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:38.220768Z",
     "iopub.status.busy": "2024-05-07T14:54:38.220110Z",
     "iopub.status.idle": "2024-05-07T14:54:38.263918Z",
     "shell.execute_reply": "2024-05-07T14:54:38.263189Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cd98b0-1ca6-4585-add5-f26880709705",
   "metadata": {},
   "source": [
    "And print the optimization results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c3ea8fa-2c67-47e1-9904-41b9b1365980",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:38.269336Z",
     "iopub.status.busy": "2024-05-07T14:54:38.268028Z",
     "iopub.status.idle": "2024-05-07T14:54:38.744179Z",
     "shell.execute_reply": "2024-05-07T14:54:38.743458Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.069</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.068</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.064</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.059</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.003</td>\n",
       "      <td>23.0</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probability  cost                              solution  count\n",
       "0         0.069   3.0  [0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0]     69\n",
       "1         0.068   3.0  [1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0]     68\n",
       "3         0.064   3.0  [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]     64\n",
       "4         0.059   3.0  [1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]     59\n",
       "39        0.003  23.0  [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]      3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    tasks_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdfaa673-443b-4277-8c65-9d079b85a010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:38.748780Z",
     "iopub.status.busy": "2024-05-07T14:54:38.747626Z",
     "iopub.status.idle": "2024-05-07T14:54:38.754879Z",
     "shell.execute_reply": "2024-05-07T14:54:38.754179Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0] , cost = 3.0\n"
     ]
    }
   ],
   "source": [
    "idx = optimization_result.cost.idxmin()\n",
    "print(\n",
    "    \"x =\", optimization_result.solution[idx], \", cost =\", optimization_result.cost[idx]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58c28f9-ab0f-4f8a-8bb4-0a219648a6b1",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14f22e03-a818-4f75-b545-54d1b504623f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:38.759527Z",
     "iopub.status.busy": "2024-05-07T14:54:38.758351Z",
     "iopub.status.idle": "2024-05-07T14:54:39.194106Z",
     "shell.execute_reply": "2024-05-07T14:54:39.193372Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e0b13e-d912-444d-9ccf-f26433db44e8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Best solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40610214-93a8-421c-899f-4f30fa4040c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:39.199077Z",
     "iopub.status.busy": "2024-05-07T14:54:39.197923Z",
     "iopub.status.idle": "2024-05-07T14:54:39.941596Z",
     "shell.execute_reply": "2024-05-07T14:54:39.940949Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qaoa_solution = np.array(\n",
    "    optimization_result.solution[optimization_result.cost.idxmin()]\n",
    ").reshape(num_timeslots, len(G.nodes))\n",
    "plot_workflow(qaoa_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec48797-9039-4580-9bab-57c36ed29159",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compare to a classical optimizer result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fc36dce-f0ae-4f27-beb1-dd84f2f7e5fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:39.945919Z",
     "iopub.status.busy": "2024-05-07T14:54:39.944840Z",
     "iopub.status.idle": "2024-05-07T14:54:40.050228Z",
     "shell.execute_reply": "2024-05-07T14:54:40.049537Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model task_scheduling\n",
      "\n",
      "  Variables:\n",
      "    x : Size=12, Index=x_index\n",
      "        Key    : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        (0, 0) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (0, 1) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (0, 2) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (0, 3) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 0) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 1) :     0 :   1.0 :     1 : False : False : Binary\n",
      "        (1, 2) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (1, 3) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 0) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 1) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 2) :     0 :   0.0 :     1 : False : False : Binary\n",
      "        (2, 3) :     0 :   1.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   3.0\n",
      "\n",
      "  Constraints:\n",
      "    all_works_are_done : Size=4\n",
      "        Key : Lower : Body : Upper\n",
      "          0 :   1.0 :  1.0 :   1.0\n",
      "          1 :   1.0 :  1.0 :   1.0\n",
      "          2 :   1.0 :  1.0 :   1.0\n",
      "          3 :   1.0 :  1.0 :   1.0\n",
      "    capacity_is_valid : Size=3\n",
      "        Key : Lower : Body : Upper\n",
      "          0 :  None :  2.0 :   3.0\n",
      "          1 :  None :  1.0 :   3.0\n",
      "          2 :  None :  1.0 :   3.0\n",
      "    works_done_by_their_order : Size=18\n",
      "        Key          : Lower : Body : Upper\n",
      "        (0, 1, 0, 0) :   0.0 :  0.0 :   0.0\n",
      "        (0, 1, 1, 0) :   0.0 :  0.0 :   0.0\n",
      "        (0, 1, 1, 1) :   0.0 :  0.0 :   0.0\n",
      "        (0, 1, 2, 0) :   0.0 :  0.0 :   0.0\n",
      "        (0, 1, 2, 1) :   0.0 :  0.0 :   0.0\n",
      "        (0, 1, 2, 2) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 0, 0) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 1, 0) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 1, 1) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 2, 0) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 2, 1) :   0.0 :  0.0 :   0.0\n",
      "        (1, 3, 2, 2) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 0, 0) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 1, 0) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 1, 1) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 2, 0) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 2, 1) :   0.0 :  0.0 :   0.0\n",
      "        (2, 3, 2, 2) :   0.0 :  0.0 :   0.0\n",
      "    eliminating_rule : Size=6\n",
      "        Key    : Lower : Body : Upper\n",
      "        (0, 1) :   0.0 :  0.0 :   0.0\n",
      "        (0, 2) :   0.0 :  0.0 :   0.0\n",
      "        (1, 0) :   0.0 :  0.0 :   0.0\n",
      "        (1, 2) :   0.0 :  0.0 :   0.0\n",
      "        (2, 2) :   0.0 :  0.0 :   0.0\n",
      "        (3, 0) :   0.0 :  0.0 :   0.0\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(tasks_model)\n",
    "\n",
    "tasks_model.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05d8716c-38ba-495c-89a8-8cc8127de211",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:40.052715Z",
     "iopub.status.busy": "2024-05-07T14:54:40.052397Z",
     "iopub.status.idle": "2024-05-07T14:54:40.678030Z",
     "shell.execute_reply": "2024-05-07T14:54:40.677336Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classical_solution = np.array(\n",
    "    [\n",
    "        int(pyo.value(tasks_model.x[idx]))\n",
    "        for idx in np.ndindex(num_timeslots, len(G.nodes))\n",
    "    ]\n",
    ").reshape(num_timeslots, len(G.nodes))\n",
    "plot_workflow(classical_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe79330-144e-469f-a342-060b884fd072",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Large Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3662eed-a27d-4b3a-a1a1-38ab4b91c61d",
   "metadata": {},
   "source": [
    "We will try a more elaborate example, involving more works with non-uniform workloads and resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d55211cd-8ad1-4814-aa53-046a1cb4ccbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:40.680597Z",
     "iopub.status.busy": "2024-05-07T14:54:40.680229Z",
     "iopub.status.idle": "2024-05-07T14:54:41.253942Z",
     "shell.execute_reply": "2024-05-07T14:54:41.253201Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tasks_model_large, G, num_timeslots, capacities, workloads = large_example()\n",
    "plot_workflow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "174bf1b6-c7b4-4767-865b-4092cc3b7b7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:41.259406Z",
     "iopub.status.busy": "2024-05-07T14:54:41.257596Z",
     "iopub.status.idle": "2024-05-07T14:54:41.287133Z",
     "shell.execute_reply": "2024-05-07T14:54:41.286408Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 Set Declarations\n",
      "    all_works_are_done_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    capacity_is_valid_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {0, 1, 2, 3, 4}\n",
      "    eliminating_rule_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain                                            : Size : Members\n",
      "        None :     2 : eliminating_rule_index_0*eliminating_rule_index_1 :   30 : {(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4)}\n",
      "    eliminating_rule_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    eliminating_rule_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {0, 1, 2, 3, 4}\n",
      "    works_done_by_their_order_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain                                                                                                                                  : Size : Members\n",
      "        None :     4 : works_done_by_their_order_index_0*works_done_by_their_order_index_1*works_done_by_their_order_index_2*works_done_by_their_order_index_3 :  900 : {(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 0), (0, 0, 1, 1), (0, 0, 1, 2), (0, 0, 1, 3), (0, 0, 1, 4), (0, 0, 2, 0), (0, 0, 2, 1), (0, 0, 2, 2), (0, 0, 2, 3), (0, 0, 2, 4), (0, 0, 3, 0), (0, 0, 3, 1), (0, 0, 3, 2), (0, 0, 3, 3), (0, 0, 3, 4), (0, 0, 4, 0), (0, 0, 4, 1), (0, 0, 4, 2), (0, 0, 4, 3), (0, 0, 4, 4), (0, 1, 0, 0), (0, 1, 0, 1), (0, 1, 0, 2), (0, 1, 0, 3), (0, 1, 0, 4), (0, 1, 1, 0), (0, 1, 1, 1), (0, 1, 1, 2), (0, 1, 1, 3), (0, 1, 1, 4), (0, 1, 2, 0), (0, 1, 2, 1), (0, 1, 2, 2), (0, 1, 2, 3), (0, 1, 2, 4), (0, 1, 3, 0), (0, 1, 3, 1), (0, 1, 3, 2), (0, 1, 3, 3), (0, 1, 3, 4), (0, 1, 4, 0), (0, 1, 4, 1), (0, 1, 4, 2), (0, 1, 4, 3), (0, 1, 4, 4), (0, 2, 0, 0), (0, 2, 0, 1), (0, 2, 0, 2), (0, 2, 0, 3), (0, 2, 0, 4), (0, 2, 1, 0), (0, 2, 1, 1), (0, 2, 1, 2), (0, 2, 1, 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(4, 4, 0, 2), (4, 4, 0, 3), (4, 4, 0, 4), (4, 4, 1, 0), (4, 4, 1, 1), (4, 4, 1, 2), (4, 4, 1, 3), (4, 4, 1, 4), (4, 4, 2, 0), (4, 4, 2, 1), (4, 4, 2, 2), (4, 4, 2, 3), (4, 4, 2, 4), (4, 4, 3, 0), (4, 4, 3, 1), (4, 4, 3, 2), (4, 4, 3, 3), (4, 4, 3, 4), (4, 4, 4, 0), (4, 4, 4, 1), (4, 4, 4, 2), (4, 4, 4, 3), (4, 4, 4, 4), (4, 5, 0, 0), (4, 5, 0, 1), (4, 5, 0, 2), (4, 5, 0, 3), (4, 5, 0, 4), (4, 5, 1, 0), (4, 5, 1, 1), (4, 5, 1, 2), (4, 5, 1, 3), (4, 5, 1, 4), (4, 5, 2, 0), (4, 5, 2, 1), (4, 5, 2, 2), (4, 5, 2, 3), (4, 5, 2, 4), (4, 5, 3, 0), (4, 5, 3, 1), (4, 5, 3, 2), (4, 5, 3, 3), (4, 5, 3, 4), (4, 5, 4, 0), (4, 5, 4, 1), (4, 5, 4, 2), (4, 5, 4, 3), (4, 5, 4, 4), (5, 0, 0, 0), (5, 0, 0, 1), (5, 0, 0, 2), (5, 0, 0, 3), (5, 0, 0, 4), (5, 0, 1, 0), (5, 0, 1, 1), (5, 0, 1, 2), (5, 0, 1, 3), (5, 0, 1, 4), (5, 0, 2, 0), (5, 0, 2, 1), (5, 0, 2, 2), (5, 0, 2, 3), (5, 0, 2, 4), (5, 0, 3, 0), (5, 0, 3, 1), (5, 0, 3, 2), (5, 0, 3, 3), (5, 0, 3, 4), (5, 0, 4, 0), (5, 0, 4, 1), (5, 0, 4, 2), (5, 0, 4, 3), (5, 0, 4, 4), (5, 1, 0, 0), (5, 1, 0, 1), (5, 1, 0, 2), (5, 1, 0, 3), (5, 1, 0, 4), (5, 1, 1, 0), (5, 1, 1, 1), (5, 1, 1, 2), (5, 1, 1, 3), (5, 1, 1, 4), (5, 1, 2, 0), (5, 1, 2, 1), (5, 1, 2, 2), (5, 1, 2, 3), (5, 1, 2, 4), (5, 1, 3, 0), (5, 1, 3, 1), (5, 1, 3, 2), (5, 1, 3, 3), (5, 1, 3, 4), (5, 1, 4, 0), (5, 1, 4, 1), (5, 1, 4, 2), (5, 1, 4, 3), (5, 1, 4, 4), (5, 2, 0, 0), (5, 2, 0, 1), (5, 2, 0, 2), (5, 2, 0, 3), (5, 2, 0, 4), (5, 2, 1, 0), (5, 2, 1, 1), (5, 2, 1, 2), (5, 2, 1, 3), (5, 2, 1, 4), (5, 2, 2, 0), (5, 2, 2, 1), (5, 2, 2, 2), (5, 2, 2, 3), (5, 2, 2, 4), (5, 2, 3, 0), (5, 2, 3, 1), (5, 2, 3, 2), (5, 2, 3, 3), (5, 2, 3, 4), (5, 2, 4, 0), (5, 2, 4, 1), (5, 2, 4, 2), (5, 2, 4, 3), (5, 2, 4, 4), (5, 3, 0, 0), (5, 3, 0, 1), (5, 3, 0, 2), (5, 3, 0, 3), (5, 3, 0, 4), (5, 3, 1, 0), (5, 3, 1, 1), (5, 3, 1, 2), (5, 3, 1, 3), (5, 3, 1, 4), (5, 3, 2, 0), (5, 3, 2, 1), (5, 3, 2, 2), (5, 3, 2, 3), (5, 3, 2, 4), (5, 3, 3, 0), (5, 3, 3, 1), (5, 3, 3, 2), (5, 3, 3, 3), (5, 3, 3, 4), (5, 3, 4, 0), (5, 3, 4, 1), (5, 3, 4, 2), (5, 3, 4, 3), (5, 3, 4, 4), (5, 4, 0, 0), (5, 4, 0, 1), (5, 4, 0, 2), (5, 4, 0, 3), (5, 4, 0, 4), (5, 4, 1, 0), (5, 4, 1, 1), (5, 4, 1, 2), (5, 4, 1, 3), (5, 4, 1, 4), (5, 4, 2, 0), (5, 4, 2, 1), (5, 4, 2, 2), (5, 4, 2, 3), (5, 4, 2, 4), (5, 4, 3, 0), (5, 4, 3, 1), (5, 4, 3, 2), (5, 4, 3, 3), (5, 4, 3, 4), (5, 4, 4, 0), (5, 4, 4, 1), (5, 4, 4, 2), (5, 4, 4, 3), (5, 4, 4, 4), (5, 5, 0, 0), (5, 5, 0, 1), (5, 5, 0, 2), (5, 5, 0, 3), (5, 5, 0, 4), (5, 5, 1, 0), (5, 5, 1, 1), (5, 5, 1, 2), (5, 5, 1, 3), (5, 5, 1, 4), (5, 5, 2, 0), (5, 5, 2, 1), (5, 5, 2, 2), (5, 5, 2, 3), (5, 5, 2, 4), (5, 5, 3, 0), (5, 5, 3, 1), (5, 5, 3, 2), (5, 5, 3, 3), (5, 5, 3, 4), (5, 5, 4, 0), (5, 5, 4, 1), (5, 5, 4, 2), (5, 5, 4, 3), (5, 5, 4, 4)}\n",
      "    works_done_by_their_order_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    works_done_by_their_order_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "    works_done_by_their_order_index_2 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {0, 1, 2, 3, 4}\n",
      "    works_done_by_their_order_index_3 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {0, 1, 2, 3, 4}\n",
      "    x_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain              : Size : Members\n",
      "        None :     2 : x_index_0*x_index_1 :   30 : {(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5)}\n",
      "    x_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    5 : {0, 1, 2, 3, 4}\n",
      "    x_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    6 : {0, 1, 2, 3, 4, 5}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=30, Index=x_index\n",
      "        Key    : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        (0, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 4) :     0 :  None :     1 : False :  True : Binary\n",
      "        (0, 5) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 4) :     0 :  None :     1 : False :  True : Binary\n",
      "        (1, 5) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 4) :     0 :  None :     1 : False :  True : Binary\n",
      "        (2, 5) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 4) :     0 :  None :     1 : False :  True : Binary\n",
      "        (3, 5) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 0) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 1) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 2) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 3) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 4) :     0 :  None :     1 : False :  True : Binary\n",
      "        (4, 5) :     0 :  None :     1 : False :  True : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : minimize : x[0,5] + 2*x[1,5] + 3*x[2,5] + 4*x[3,5] + 5*x[4,5]\n",
      "\n",
      "4 Constraint Declarations\n",
      "    all_works_are_done : Size=6, Index=all_works_are_done_index, Active=True\n",
      "        Key : Lower : Body                                       : Upper : Active\n",
      "          0 :   1.0 : x[0,0] + x[1,0] + x[2,0] + x[3,0] + x[4,0] :   1.0 :   True\n",
      "          1 :   1.0 : x[0,1] + x[1,1] + x[2,1] + x[3,1] + x[4,1] :   1.0 :   True\n",
      "          2 :   1.0 : x[0,2] + x[1,2] + x[2,2] + x[3,2] + x[4,2] :   1.0 :   True\n",
      "          3 :   1.0 : x[0,3] + x[1,3] + x[2,3] + x[3,3] + x[4,3] :   1.0 :   True\n",
      "          4 :   1.0 : x[0,4] + x[1,4] + x[2,4] + x[3,4] + x[4,4] :   1.0 :   True\n",
      "          5 :   1.0 : x[0,5] + x[1,5] + x[2,5] + x[3,5] + x[4,5] :   1.0 :   True\n",
      "    capacity_is_valid : Size=5, Index=capacity_is_valid_index, Active=True\n",
      "        Key : Lower : Body                                                      : Upper : Active\n",
      "          0 :  -Inf : x[0,0] + 3*x[0,1] + 2*x[0,2] + 2*x[0,3] + x[0,4] + x[0,5] :   1.0 :   True\n",
      "          1 :  -Inf : x[1,0] + 3*x[1,1] + 2*x[1,2] + 2*x[1,3] + x[1,4] + x[1,5] :   3.0 :   True\n",
      "          2 :  -Inf : x[2,0] + 3*x[2,1] + 2*x[2,2] + 2*x[2,3] + x[2,4] + x[2,5] :   4.0 :   True\n",
      "          3 :  -Inf : x[3,0] + 3*x[3,1] + 2*x[3,2] + 2*x[3,3] + x[3,4] + x[3,5] :   3.0 :   True\n",
      "          4 :  -Inf : x[4,0] + 3*x[4,1] + 2*x[4,2] + 2*x[4,3] + x[4,4] + x[4,5] :   1.0 :   True\n",
      "    eliminating_rule : Size=15, Index=eliminating_rule_index, Active=True\n",
      "        Key    : Lower : Body   : Upper : Active\n",
      "        (0, 3) :   0.0 : x[3,0] :   0.0 :   True\n",
      "        (0, 4) :   0.0 : x[4,0] :   0.0 :   True\n",
      "        (1, 0) :   0.0 : x[0,1] :   0.0 :   True\n",
      "        (1, 4) :   0.0 : x[4,1] :   0.0 :   True\n",
      "        (2, 0) :   0.0 : x[0,2] :   0.0 :   True\n",
      "        (2, 3) :   0.0 : x[3,2] :   0.0 :   True\n",
      "        (2, 4) :   0.0 : x[4,2] :   0.0 :   True\n",
      "        (3, 0) :   0.0 : x[0,3] :   0.0 :   True\n",
      "        (3, 3) :   0.0 : x[3,3] :   0.0 :   True\n",
      "        (3, 4) :   0.0 : x[4,3] :   0.0 :   True\n",
      "        (4, 0) :   0.0 : x[0,4] :   0.0 :   True\n",
      "        (4, 1) :   0.0 : x[1,4] :   0.0 :   True\n",
      "        (4, 4) :   0.0 : x[4,4] :   0.0 :   True\n",
      "        (5, 0) :   0.0 : x[0,5] :   0.0 :   True\n",
      "        (5, 1) :   0.0 : x[1,5] :   0.0 :   True\n",
      "    works_done_by_their_order : Size=105, Index=works_done_by_their_order_index, Active=True\n",
      "        Key          : Lower : Body          : Upper : Active\n",
      "        (0, 1, 0, 0) :   0.0 : x[0,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 1, 0) :   0.0 : x[1,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 1, 1) :   0.0 : x[1,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 2, 0) :   0.0 : x[2,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 2, 1) :   0.0 : x[2,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 2, 2) :   0.0 : x[2,0]*x[2,1] :   0.0 :   True\n",
      "        (0, 1, 3, 0) :   0.0 : x[3,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 3, 1) :   0.0 : x[3,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 3, 2) :   0.0 : x[3,0]*x[2,1] :   0.0 :   True\n",
      "        (0, 1, 3, 3) :   0.0 : x[3,0]*x[3,1] :   0.0 :   True\n",
      "        (0, 1, 4, 0) :   0.0 : x[4,0]*x[0,1] :   0.0 :   True\n",
      "        (0, 1, 4, 1) :   0.0 : x[4,0]*x[1,1] :   0.0 :   True\n",
      "        (0, 1, 4, 2) :   0.0 : x[4,0]*x[2,1] :   0.0 :   True\n",
      "        (0, 1, 4, 3) :   0.0 : x[4,0]*x[3,1] :   0.0 :   True\n",
      "        (0, 1, 4, 4) :   0.0 : x[4,0]*x[4,1] :   0.0 :   True\n",
      "        (0, 2, 0, 0) :   0.0 : x[0,0]*x[0,2] :   0.0 :   True\n",
      "        (0, 2, 1, 0) :   0.0 : x[1,0]*x[0,2] :   0.0 :   True\n",
      "        (0, 2, 1, 1) :   0.0 : x[1,0]*x[1,2] :   0.0 :   True\n",
      "        (0, 2, 2, 0) :   0.0 : x[2,0]*x[0,2] :   0.0 :   True\n",
      "        (0, 2, 2, 1) :   0.0 : x[2,0]*x[1,2] :   0.0 :   True\n",
      "        (0, 2, 2, 2) :   0.0 : x[2,0]*x[2,2] :   0.0 :   True\n",
      "        (0, 2, 3, 0) :   0.0 : x[3,0]*x[0,2] :   0.0 :   True\n",
      "        (0, 2, 3, 1) :   0.0 : x[3,0]*x[1,2] :   0.0 :   True\n",
      "        (0, 2, 3, 2) :   0.0 : x[3,0]*x[2,2] :   0.0 :   True\n",
      "        (0, 2, 3, 3) :   0.0 : x[3,0]*x[3,2] :   0.0 :   True\n",
      "        (0, 2, 4, 0) :   0.0 : x[4,0]*x[0,2] :   0.0 :   True\n",
      "        (0, 2, 4, 1) :   0.0 : x[4,0]*x[1,2] :   0.0 :   True\n",
      "        (0, 2, 4, 2) :   0.0 : x[4,0]*x[2,2] :   0.0 :   True\n",
      "        (0, 2, 4, 3) :   0.0 : x[4,0]*x[3,2] :   0.0 :   True\n",
      "        (0, 2, 4, 4) :   0.0 : x[4,0]*x[4,2] :   0.0 :   True\n",
      "        (0, 3, 0, 0) :   0.0 : x[0,0]*x[0,3] :   0.0 :   True\n",
      "        (0, 3, 1, 0) :   0.0 : x[1,0]*x[0,3] :   0.0 :   True\n",
      "        (0, 3, 1, 1) :   0.0 : x[1,0]*x[1,3] :   0.0 :   True\n",
      "        (0, 3, 2, 0) :   0.0 : x[2,0]*x[0,3] :   0.0 :   True\n",
      "        (0, 3, 2, 1) :   0.0 : x[2,0]*x[1,3] :   0.0 :   True\n",
      "        (0, 3, 2, 2) :   0.0 : x[2,0]*x[2,3] :   0.0 :   True\n",
      "        (0, 3, 3, 0) :   0.0 : x[3,0]*x[0,3] :   0.0 :   True\n",
      "        (0, 3, 3, 1) :   0.0 : x[3,0]*x[1,3] :   0.0 :   True\n",
      "        (0, 3, 3, 2) :   0.0 : x[3,0]*x[2,3] :   0.0 :   True\n",
      "        (0, 3, 3, 3) :   0.0 : x[3,0]*x[3,3] :   0.0 :   True\n",
      "        (0, 3, 4, 0) :   0.0 : x[4,0]*x[0,3] :   0.0 :   True\n",
      "        (0, 3, 4, 1) :   0.0 : x[4,0]*x[1,3] :   0.0 :   True\n",
      "        (0, 3, 4, 2) :   0.0 : x[4,0]*x[2,3] :   0.0 :   True\n",
      "        (0, 3, 4, 3) :   0.0 : x[4,0]*x[3,3] :   0.0 :   True\n",
      "        (0, 3, 4, 4) :   0.0 : x[4,0]*x[4,3] :   0.0 :   True\n",
      "        (1, 5, 0, 0) :   0.0 : x[0,1]*x[0,5] :   0.0 :   True\n",
      "        (1, 5, 1, 0) :   0.0 : x[1,1]*x[0,5] :   0.0 :   True\n",
      "        (1, 5, 1, 1) :   0.0 : x[1,1]*x[1,5] :   0.0 :   True\n",
      "        (1, 5, 2, 0) :   0.0 : x[2,1]*x[0,5] :   0.0 :   True\n",
      "        (1, 5, 2, 1) :   0.0 : x[2,1]*x[1,5] :   0.0 :   True\n",
      "        (1, 5, 2, 2) :   0.0 : x[2,1]*x[2,5] :   0.0 :   True\n",
      "        (1, 5, 3, 0) :   0.0 : x[3,1]*x[0,5] :   0.0 :   True\n",
      "        (1, 5, 3, 1) :   0.0 : x[3,1]*x[1,5] :   0.0 :   True\n",
      "        (1, 5, 3, 2) :   0.0 : x[3,1]*x[2,5] :   0.0 :   True\n",
      "        (1, 5, 3, 3) :   0.0 : x[3,1]*x[3,5] :   0.0 :   True\n",
      "        (1, 5, 4, 0) :   0.0 : x[4,1]*x[0,5] :   0.0 :   True\n",
      "        (1, 5, 4, 1) :   0.0 : x[4,1]*x[1,5] :   0.0 :   True\n",
      "        (1, 5, 4, 2) :   0.0 : x[4,1]*x[2,5] :   0.0 :   True\n",
      "        (1, 5, 4, 3) :   0.0 : x[4,1]*x[3,5] :   0.0 :   True\n",
      "        (1, 5, 4, 4) :   0.0 : x[4,1]*x[4,5] :   0.0 :   True\n",
      "        (2, 4, 0, 0) :   0.0 : x[0,2]*x[0,4] :   0.0 :   True\n",
      "        (2, 4, 1, 0) :   0.0 : x[1,2]*x[0,4] :   0.0 :   True\n",
      "        (2, 4, 1, 1) :   0.0 : x[1,2]*x[1,4] :   0.0 :   True\n",
      "        (2, 4, 2, 0) :   0.0 : x[2,2]*x[0,4] :   0.0 :   True\n",
      "        (2, 4, 2, 1) :   0.0 : x[2,2]*x[1,4] :   0.0 :   True\n",
      "        (2, 4, 2, 2) :   0.0 : x[2,2]*x[2,4] :   0.0 :   True\n",
      "        (2, 4, 3, 0) :   0.0 : x[3,2]*x[0,4] :   0.0 :   True\n",
      "        (2, 4, 3, 1) :   0.0 : x[3,2]*x[1,4] :   0.0 :   True\n",
      "        (2, 4, 3, 2) :   0.0 : x[3,2]*x[2,4] :   0.0 :   True\n",
      "        (2, 4, 3, 3) :   0.0 : x[3,2]*x[3,4] :   0.0 :   True\n",
      "        (2, 4, 4, 0) :   0.0 : x[4,2]*x[0,4] :   0.0 :   True\n",
      "        (2, 4, 4, 1) :   0.0 : x[4,2]*x[1,4] :   0.0 :   True\n",
      "        (2, 4, 4, 2) :   0.0 : x[4,2]*x[2,4] :   0.0 :   True\n",
      "        (2, 4, 4, 3) :   0.0 : x[4,2]*x[3,4] :   0.0 :   True\n",
      "        (2, 4, 4, 4) :   0.0 : x[4,2]*x[4,4] :   0.0 :   True\n",
      "        (3, 4, 0, 0) :   0.0 : x[0,3]*x[0,4] :   0.0 :   True\n",
      "        (3, 4, 1, 0) :   0.0 : x[1,3]*x[0,4] :   0.0 :   True\n",
      "        (3, 4, 1, 1) :   0.0 : x[1,3]*x[1,4] :   0.0 :   True\n",
      "        (3, 4, 2, 0) :   0.0 : x[2,3]*x[0,4] :   0.0 :   True\n",
      "        (3, 4, 2, 1) :   0.0 : x[2,3]*x[1,4] :   0.0 :   True\n",
      "        (3, 4, 2, 2) :   0.0 : x[2,3]*x[2,4] :   0.0 :   True\n",
      "        (3, 4, 3, 0) :   0.0 : x[3,3]*x[0,4] :   0.0 :   True\n",
      "        (3, 4, 3, 1) :   0.0 : x[3,3]*x[1,4] :   0.0 :   True\n",
      "        (3, 4, 3, 2) :   0.0 : x[3,3]*x[2,4] :   0.0 :   True\n",
      "        (3, 4, 3, 3) :   0.0 : x[3,3]*x[3,4] :   0.0 :   True\n",
      "        (3, 4, 4, 0) :   0.0 : x[4,3]*x[0,4] :   0.0 :   True\n",
      "        (3, 4, 4, 1) :   0.0 : x[4,3]*x[1,4] :   0.0 :   True\n",
      "        (3, 4, 4, 2) :   0.0 : x[4,3]*x[2,4] :   0.0 :   True\n",
      "        (3, 4, 4, 3) :   0.0 : x[4,3]*x[3,4] :   0.0 :   True\n",
      "        (3, 4, 4, 4) :   0.0 : x[4,3]*x[4,4] :   0.0 :   True\n",
      "        (4, 5, 0, 0) :   0.0 : x[0,4]*x[0,5] :   0.0 :   True\n",
      "        (4, 5, 1, 0) :   0.0 : x[1,4]*x[0,5] :   0.0 :   True\n",
      "        (4, 5, 1, 1) :   0.0 : x[1,4]*x[1,5] :   0.0 :   True\n",
      "        (4, 5, 2, 0) :   0.0 : x[2,4]*x[0,5] :   0.0 :   True\n",
      "        (4, 5, 2, 1) :   0.0 : x[2,4]*x[1,5] :   0.0 :   True\n",
      "        (4, 5, 2, 2) :   0.0 : x[2,4]*x[2,5] :   0.0 :   True\n",
      "        (4, 5, 3, 0) :   0.0 : x[3,4]*x[0,5] :   0.0 :   True\n",
      "        (4, 5, 3, 1) :   0.0 : x[3,4]*x[1,5] :   0.0 :   True\n",
      "        (4, 5, 3, 2) :   0.0 : x[3,4]*x[2,5] :   0.0 :   True\n",
      "        (4, 5, 3, 3) :   0.0 : x[3,4]*x[3,5] :   0.0 :   True\n",
      "        (4, 5, 4, 0) :   0.0 : x[4,4]*x[0,5] :   0.0 :   True\n",
      "        (4, 5, 4, 1) :   0.0 : x[4,4]*x[1,5] :   0.0 :   True\n",
      "        (4, 5, 4, 2) :   0.0 : x[4,4]*x[2,5] :   0.0 :   True\n",
      "        (4, 5, 4, 3) :   0.0 : x[4,4]*x[3,5] :   0.0 :   True\n",
      "        (4, 5, 4, 4) :   0.0 : x[4,4]*x[4,5] :   0.0 :   True\n",
      "\n",
      "19 Declarations: x_index_0 x_index_1 x_index x all_works_are_done_index all_works_are_done capacity_is_valid_index capacity_is_valid works_done_by_their_order_index_0 works_done_by_their_order_index_1 works_done_by_their_order_index_2 works_done_by_their_order_index_3 works_done_by_their_order_index works_done_by_their_order eliminating_rule_index_0 eliminating_rule_index_1 eliminating_rule_index eliminating_rule cost\n"
     ]
    }
   ],
   "source": [
    "tasks_model_large.pprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecad1485-a5b6-411f-ade0-95777ca32978",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:41.291956Z",
     "iopub.status.busy": "2024-05-07T14:54:41.290738Z",
     "iopub.status.idle": "2024-05-07T14:54:53.384061Z",
     "shell.execute_reply": "2024-05-07T14:54:53.383267Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import (\n",
    "    construct_combinatorial_optimization_model,\n",
    "    execute,\n",
    "    set_execution_preferences,\n",
    "    show,\n",
    "    synthesize,\n",
    ")\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "from classiq.execution import ExecutionPreferences, IBMBackendPreferences\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=8, penalty_energy=20.0)\n",
    "\n",
    "optimizer_config = OptimizerConfig(max_iteration=1, alpha_cvar=0.6)\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=tasks_model_large,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16b36025-9e47-4401-9c19-ab3ea07a342c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:53.389274Z",
     "iopub.status.busy": "2024-05-07T14:54:53.388010Z",
     "iopub.status.idle": "2024-05-07T14:54:53.769824Z",
     "shell.execute_reply": "2024-05-07T14:54:53.769083Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"task_scheduling_problem_large\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b112b6a7-2b80-4d4e-ad00-214afca35d73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:54:53.778526Z",
     "iopub.status.busy": "2024-05-07T14:54:53.777348Z",
     "iopub.status.idle": "2024-05-07T14:55:20.296806Z",
     "shell.execute_reply": "2024-05-07T14:55:20.296069Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/d48a3b5a-103e-402e-b81c-544840912860?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4332e206-5381-41bc-a149-c7571195a600",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:55:20.302040Z",
     "iopub.status.busy": "2024-05-07T14:55:20.300616Z",
     "iopub.status.idle": "2024-05-07T14:58:42.709063Z",
     "shell.execute_reply": "2024-05-07T14:58:42.708338Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5cc31f-7f51-45cc-b1de-88a7ee57fcbc",
   "metadata": {},
   "source": [
    "As the search space here is much larger and involving many qubits, the optimizer takes much more time and might not converge to a legal solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec39e7b8-6b2d-43e2-8f6f-14cdd064b7b5",
   "metadata": {},
   "source": [
    "And print the optimization results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1beec214-9c5d-4759-a195-31adb5e0c4dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:42.714337Z",
     "iopub.status.busy": "2024-05-07T14:58:42.712937Z",
     "iopub.status.idle": "2024-05-07T14:58:55.015436Z",
     "shell.execute_reply": "2024-05-07T14:58:55.014683Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.001</td>\n",
       "      <td>105.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>0.001</td>\n",
       "      <td>105.0</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.001</td>\n",
       "      <td>140.0</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>0.001</td>\n",
       "      <td>140.0</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.001</td>\n",
       "      <td>140.0</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability   cost                                           solution  \\\n",
       "20         0.001  105.0  [0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "292        0.001  105.0  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, ...   \n",
       "17         0.001  140.0  [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, ...   \n",
       "777        0.001  140.0  [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "978        0.001  140.0  [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, ...   \n",
       "\n",
       "     count  \n",
       "20       1  \n",
       "292      1  \n",
       "17       1  \n",
       "777      1  \n",
       "978      1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    tasks_model_large,\n",
    "    vqe_result=res[0].value,\n",
    "    penalty_energy=qaoa_config.penalty_energy,\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3efad6e6-c4e3-4b48-85df-417b3425a52f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:55.020145Z",
     "iopub.status.busy": "2024-05-07T14:58:55.018973Z",
     "iopub.status.idle": "2024-05-07T14:58:55.025980Z",
     "shell.execute_reply": "2024-05-07T14:58:55.025423Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0] , cost = 104.99999999999997\n"
     ]
    }
   ],
   "source": [
    "idx = optimization_result.cost.idxmin()\n",
    "print(\n",
    "    \"x =\", optimization_result.solution[idx], \", cost =\", optimization_result.cost[idx]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958fb9fa-a657-42e5-8b31-a0a0888879ca",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f93885e1-9ceb-4874-b366-2d432d9ba2a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:55.030122Z",
     "iopub.status.busy": "2024-05-07T14:58:55.029040Z",
     "iopub.status.idle": "2024-05-07T14:58:55.301968Z",
     "shell.execute_reply": "2024-05-07T14:58:55.301240Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144dd535-38c3-4976-9f9c-21df694fe8ed",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Best solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a641e263-61a6-46a1-90e6-e3e3ccd0c10f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:55.307551Z",
     "iopub.status.busy": "2024-05-07T14:58:55.306015Z",
     "iopub.status.idle": "2024-05-07T14:58:56.199240Z",
     "shell.execute_reply": "2024-05-07T14:58:56.198581Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qaoa_solution_large = np.array(\n",
    "    optimization_result.solution[optimization_result.cost.idxmin()]\n",
    ").reshape(num_timeslots, len(G.nodes))\n",
    "plot_workflow(qaoa_solution_large)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0afce92-dd77-4c41-88ba-8047305efe00",
   "metadata": {},
   "source": [
    "## Classical solution for the large problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4b8015d-4086-4c71-a52e-d0d6ad7c6c63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:56.204225Z",
     "iopub.status.busy": "2024-05-07T14:58:56.202893Z",
     "iopub.status.idle": "2024-05-07T14:58:56.281401Z",
     "shell.execute_reply": "2024-05-07T14:58:56.280663Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model task_scheduling\n",
      "\n",
      "  Variables:\n",
      "    x : Size=30, Index=x_index\n",
      "        Key    : Lower : Value                  : Upper : Fixed : Stale : Domain\n",
      "        (0, 0) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "        (0, 1) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (0, 2) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (0, 3) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (0, 4) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (0, 5) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (1, 0) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (1, 1) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "        (1, 2) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (1, 3) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (1, 4) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (1, 5) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (2, 0) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (2, 1) :     0 : -6.666389110910511e-13 :     1 : False : False : Binary\n",
      "        (2, 2) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "        (2, 3) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "        (2, 4) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (2, 5) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (3, 0) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (3, 1) :     0 :   6.66577903984944e-13 :     1 : False : False : Binary\n",
      "        (3, 2) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (3, 3) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (3, 4) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "        (3, 5) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (4, 0) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (4, 1) :     0 :  6.100710610707113e-17 :     1 : False : False : Binary\n",
      "        (4, 2) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (4, 3) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (4, 4) :     0 :                    0.0 :     1 : False : False : Binary\n",
      "        (4, 5) :     0 :                    1.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   5.0\n",
      "\n",
      "  Constraints:\n",
      "    all_works_are_done : Size=6\n",
      "        Key : Lower : Body : Upper\n",
      "          0 :   1.0 :  1.0 :   1.0\n",
      "          1 :   1.0 :  1.0 :   1.0\n",
      "          2 :   1.0 :  1.0 :   1.0\n",
      "          3 :   1.0 :  1.0 :   1.0\n",
      "          4 :   1.0 :  1.0 :   1.0\n",
      "          5 :   1.0 :  1.0 :   1.0\n",
      "    capacity_is_valid : Size=5\n",
      "        Key : Lower : Body               : Upper\n",
      "          0 :  None :                1.0 :   1.0\n",
      "          1 :  None :                3.0 :   3.0\n",
      "          2 :  None :     3.999999999998 :   4.0\n",
      "          3 :  None : 1.0000000000019997 :   3.0\n",
      "          4 :  None : 1.0000000000000002 :   1.0\n",
      "    works_done_by_their_order : Size=105\n",
      "        Key          : Lower : Body                  : Upper\n",
      "        (0, 1, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 2, 2) :   0.0 :                  -0.0 :   0.0\n",
      "        (0, 1, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 3, 2) :   0.0 :                  -0.0 :   0.0\n",
      "        (0, 1, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 4, 2) :   0.0 :                  -0.0 :   0.0\n",
      "        (0, 1, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 1, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 2, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 2, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 2, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 3, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 2, 0) :   0.0 :                  -0.0 :   0.0\n",
      "        (1, 5, 2, 1) :   0.0 :                  -0.0 :   0.0\n",
      "        (1, 5, 2, 2) :   0.0 :                  -0.0 :   0.0\n",
      "        (1, 5, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 5, 4, 4) :   0.0 : 6.100710610707113e-17 :   0.0\n",
      "        (2, 4, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 2, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 2, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 0, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 1, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 2, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 2, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 3, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 3, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 4, 2) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 4, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 5, 4, 4) :   0.0 :                   0.0 :   0.0\n",
      "    eliminating_rule : Size=15\n",
      "        Key    : Lower : Body                  : Upper\n",
      "        (0, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (0, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (1, 4) :   0.0 : 6.100710610707113e-17 :   0.0\n",
      "        (2, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (2, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 3) :   0.0 :                   0.0 :   0.0\n",
      "        (3, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 1) :   0.0 :                   0.0 :   0.0\n",
      "        (4, 4) :   0.0 :                   0.0 :   0.0\n",
      "        (5, 0) :   0.0 :                   0.0 :   0.0\n",
      "        (5, 1) :   0.0 :                   0.0 :   0.0\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(tasks_model_large)\n",
    "\n",
    "tasks_model_large.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f993e7f2-24f7-4865-93d3-dc6885f0e2c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:58:56.286086Z",
     "iopub.status.busy": "2024-05-07T14:58:56.284879Z",
     "iopub.status.idle": "2024-05-07T14:58:57.443487Z",
     "shell.execute_reply": "2024-05-07T14:58:57.442769Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classical_solution = np.array(\n",
    "    [\n",
    "        int(pyo.value(tasks_model_large.x[idx]))\n",
    "        for idx in np.ndindex(num_timeslots, len(G.nodes))\n",
    "    ]\n",
    ").reshape(num_timeslots, len(G.nodes))\n",
    "plot_workflow(classical_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21e2517-6b83-4f03-8498-77205ba46af9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## References\n",
    "\n",
    "<a id='TaskWorkflow'>[1]</a>: [Pakhomchik et. al. \"Solving workflow scheduling problems with QUBO modeling.\" arXiv preprint arXiv:2205.04844 (2022).](https://arxiv.org/pdf/2205.04844.pdf)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
